SpringerPlus· 2013
Principled missing data methods for researchers
- 2,193citations
- 2013year
Short summary
Multiple imputation, full information maximum likelihood, and expectation-maximization algorithm yield less biased estimates than listwise deletion for quantitative research with missing data.
AI-generated from the title and abstract; the full text is not read.
Key points
- Principled methods (multiple imputation, FIML, EM) reduce bias and information loss from missing data.
- These methods outperform listwise deletion in quantitative research.
- Explicit acknowledgment of missing data and its handling is crucial for research integrity.
- Manuscript review standards should incorporate appropriate treatment of missing data.
AI-generated from the title and abstract; the full text is not read.
Abstract
The impact of missing data on quantitative research can be serious, leading to biased estimates of parameters, loss of information, decreased statistical power, increased standard errors, and weakened generalizability of findings. In this paper, we discussed and demonstrated three principled missing data methods: multiple imputation, full information maximum likelihood, and expectation-maximization algorithm, applied to a real-world data set. Results were contrasted with those obtained from the complete data set and from the listwise deletion method. The relative merits of each method are noted, along with common features they share. The paper concludes with an emphasis on the importance of statistical assumptions, and recommendations for researchers. Quality of research will be enhanced if (a) researchers explicitly acknowledge missing data problems and the conditions under which they occurred, (b) principled methods are employed to handle missing data, and (c) the appropriate treatment of missing data is incorporated into review standards of manuscripts submitted for publication.
The authors' abstract, as published at the source. SpringerPlus, 2013 · DOI ↗
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Field: Statistics and Probability
Statistics and ProbabilityMathematics